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v0.6.0 (2024-09-26)

Changes:

  • add support for QR solver, preview of GLM.jl version 2.
  • add support for the dropcollinear keyword argument, following GLM.jl version 1.9. Setting it to true improves solving problems where the model matrix is not full rank.

Dependencies and docs:

  • minimal Julia version bumped to 1.6
  • use JuliaFormatter.jl
  • add typos checks

Tests:

  • improve tests output

v0.5.0 (2023-05-12)

Breaking changes:

  • remove TableRegressionModel wrapper, following GLM.jl [#31]

Other changes:

  • Add loss functions (CatoniNarrowLoss, CatoniWideLoss, HardThresholdLoss, HampelLoss)
  • Add the wobs function to use instead of nobs, take the weights into account. nobs return an Int, the number of non-zeros weights or length(response(m)) without weights.
  • Improve parameter changes with refit!
  • Improve weights (wts) usage
  • RidgePred correct various functions (dof, stderror, ...)
  • PredCG: improve perf
  • Add GLM.DensePredQR

Dependencies and docs:

  • Improve loss functions documentation
  • Reformat code, create new files (tools.jl, losses.jl, regularizedpred.jl)
  • Update dependencies compat versions (StatsBase-v0.34, StatsModels-v0.7)
  • Add dependencies (Missings-v1, StatsAPI-v1.3, Tables-v1)

Tests:

  • Tests: more systematic tests
  • Tests: add exact Ridge test.
  • Tests: add weights test

Bugfixes:

  • Fix missing type leading to StackOverflow [#17]
  • Fix infinite loop [#33]

v0.4.5 (2022-09-07)

  • Update dependencies compat versions (Roots)

v0.4.4 (2022-09-07)

  • Export hasintercept function
  • Correct nulldeviance and nullloglikelihood for models without intercept (JuliaStats/StatsAPI.jl#14).
  • Update dependencies compat versions (Tulip)

v0.4.3 (2021-10-22)

  • Add dependencies compat versions
  • Register package

v0.4.2 (2021-10-22)

  • Minimal compatibility set to julia 1.3 (because of Tulip.jl>=0.8)

v0.4.1 (2021-09-19)

  • Correctly handle multidimensional arrays with univariate robust functions.
  • Correct code formatting.

v0.4.0 (2021-09-17)

  • Drop the heavy JuMP dependency and use Tulip with the unstable internal API instead.
  • Add univariate robust functions: mean, std, var, sem, mean_and_std, mean_and_var and mean_and_sem.
  • Small bug corrections.

v0.3.0 (2021-03-22)

  • BREAKING: Implement the loss functions as subclasses of LossFunction and estimators as subclasses of AbstractEstimator. The kind keyword argument is not used anymore, instead use rlm(form, data, MMEstimator{TukeyLoss}(); initial_scale=:L1)
  • Implement Robust Ridge regression by using the keyword argument ridgeλ (and ridgeG and βprior for more general penalty).
  • Add documentation.

v0.2.0 (2020-06-04)

  • τ-Estimator
  • New estimator function: optimal Yohai-Zamar estimator
  • Resampling algorithm to find the global minimum of S- and τ-estimators.

v0.1.0 (2020-05-13)

First public release:

  • M-Estimator
  • S-Estimator
  • MM-Estimator
  • M-Quantile (Expectile, etc...)
  • Quantile regression with interior point